Coupling Load-Following Control With OPF

Bazrafshan, Mohammadhafez; Gatsis, Nikolaos; Taha, Ahmad F.; Taylor, Joshua A.

doi:10.1109/tsg.2018.2802723

Cited by 12 publications

(5 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the traditional optimal power system dispatching methods, the optimal power flow (OPF) can directly achieve the optimal computation of dispatching schemes under various constraints. Moreover, the operational optimization with more controllable variables can also be considered in OPF (Bazrafshan et al, 2019;Nojavan and Seyedi, 2020;Davoodi et al, 2021).…”

Section: Intelligent Dispatching Problem Of Power Systemmentioning

confidence: 99%

Power system intelligent operation knowledge learning model based on reinforcement learning and data-driven

Zhou

et al. 2023

Front. Energy Res.

View full text Add to dashboard Cite

With the expansion of power grid scale and the deepening of component coupling, the operation behavior of power system becomes more and more complex, and the traditional function decoupling dispatching architecture is not available anymore. Firstly, this paper studies the corresponding relationship between reinforcement learning method and power system dispatching decision problem, and constructs the artificial intelligent dispatching knowledge learning model of power system based on reinforcement learning (AIDLM). Then, a data-driven intelligent dispatching knowledge learning method is proposed, and interpretable dispatching decision knowledge is obtained. Finally, a knowledge efficiency evaluation indexes is proposed and used to guide the extraction of original acquired knowledge. The intelligent economic dispatching problem of a regional power grid is analyzed. The results show that the AIDLM method can intelligently give the dispatching strategy of power generation according to the time series changing load, which effectively reduces the cost of power generation in the grid. The method proposed in this paper can make up for the shortcomings of traditional dispatching methods and provide strong support for modern power system dispatching.

show abstract

Section: Intelligent Dispatching Problem Of Power Systemmentioning

confidence: 99%

Power system intelligent operation knowledge learning model based on reinforcement learning and data-driven

Zhou

et al. 2023

Front. Energy Res.

View full text Add to dashboard Cite

show abstract

“…Readers are referred to Appendix C for the proof of Proposition 1. In contrast to matrix inequality (11), the one given in (12) constitutes an LMI and therefore can be easily solved through standard convex optimization packages.…”

Section: B Stabilization Of Power Network's Ndaesmentioning

confidence: 99%

“…The study [12] combines the optimal power flow problem with LFC using linear quadratic regulator (LQR). Recently, a method developed using the notion of L ∞ stability is proposed in [13] to implement a robust control architecture for LRFC in power systems.…”

Section: Introductionmentioning

confidence: 99%

Load and Renewable-Following Control of Linearization-Free Differential Algebraic Equation Power System Models

Nugroho¹,

Taha²

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Electromechanical transients in power networks are mostly caused by mismatch between power consumption and production, causing generators to deviate from the nominal frequency. To that end, feedback control algorithms have been designed to perform frequency and load/renewables-following control. In particular, the literature addressed a plethora of grid-and frequency-control challenges with a focus on linearized, differential equation models whereby algebraic constraints (i.e., power flows) are eliminated. This is in contrast with the more realistic nonlinear differential algebraic equation (NDAE) models. Yet, as grids are increasingly pushed to their limits via intermittent renewables and varying loads, their physical states risk escaping operating regions due to either a poor prediction or sudden changes in renewables or demands-deeming a feedback controller based on a linearization point virtually unusable. In lieu of linearized differential equation models, the objective of this paper is to design a simple, purely decentralized, linearizationfree, feedback control law for NDAE models of power networks. The objective of such controller is to primarily stabilize frequency oscillations after a large, unknown disturbance in renewables or loads. Although the controller design involves advanced NDAE system theory, the controller itself is as simple as a decentralized proportional or linear quadratic regulator in its implementation. Case studies demonstrate that the proposed controller is able to stabilize dynamic and algebraic states under significant disturbances.

show abstract

“…In this paper, we focus on the small-signal stability of uncertain power systems. By linearizing (1) and (2) around the operating point {x 0 si , u 0 si , a 0 si }, we obtain the following dynamics of the small-signal system, with ∆x s := x(t) − x 0 ∆ ẋsi = A si ∆x si + B si ∆u si + D si ∆a si (3) where A si , B si , D si are the Jacobian matrices corresponding to the linearization of the dynamics of synchronous generator i around the operating point {x 0 si , u 0 si , a 0 si }. Similarly, (2) can be linearized around {x 0 si , u 0 si , a 0 si } as follows…”

Section: A Synchronous Generator Modelmentioning

confidence: 99%

“…Case studies are presented showcasing the performance of the L∞ controllers in comparison with automatic generation control and H∞ control methods. studies the design of grid operating points for generators with lower operational costs and desirable stability properties [2], [3]. The third category pertains to the design of economic incentives and demand-response methods that drive users to consume less energy, thereby impacting the overall grid generation and the stability of the grid [4].…”

mentioning

confidence: 99%

Robust Control for Renewable-Integrated Power Networks Considering Input Bound Constraints and Worst Case Uncertainty Measure

Taha

Bazrafshan

Nugroho

et al. 2019

IEEE Trans. Control Netw. Syst.

Self Cite

View full text Add to dashboard Cite

Uncertainty from renewable energy and loads is one of the major challenges for stable grid operation. Various approaches have been explored to remedy these uncertainties. In this paper, we design centralized or decentralized statefeedback controllers for generators while considering worst-case uncertainty. Specifically, this paper introduces the notion of L∞ robust control and stability for uncertain power networks. Uncertain and nonlinear differential algebraic equation model of the network is presented. The model includes unknown disturbances from renewables and loads. Given an operating point, the linearized state-space presentation is given. Then, the notion of L∞ robust control and stability is discussed, resulting in a nonconvex optimization routine that yields a state feedback gain mitigating the impact of disturbances. The developed routine includes explicit input-bound constraints on generators' inputs and a measure of the worst-case disturbance. The feedback control architecture can be centralized, distributed, or decentralized. Algorithms based on successive convex approximations are then given to address the nonconvexity. Case studies are presented showcasing the performance of the L∞ controllers in comparison with automatic generation control and H∞ control methods.

show abstract

Coupling Load-Following Control With OPF

Cited by 12 publications

References 37 publications

Power system intelligent operation knowledge learning model based on reinforcement learning and data-driven

Power system intelligent operation knowledge learning model based on reinforcement learning and data-driven

Load and Renewable-Following Control of Linearization-Free Differential Algebraic Equation Power System Models

Robust Control for Renewable-Integrated Power Networks Considering Input Bound Constraints and Worst Case Uncertainty Measure

Contact Info

Product

Resources

About